Toward a neuroimaging treatment selection biomarker for major depressive disorder

Mapping depression rating scale phenotypes onto research domain criteria (RDoC) to inform biological research in mood disorders

BACKGROUND

Substantial research progress can be achieved if available clinical datasets can be mapped to the National Institute of Mental Health Research-Domain-Criteria (RDoC) constructs. This mapping would allow investigators to both explore more narrowly defined clinical phenotypes and the relationship of these phenotypes to biological markers and clinical outcomes approximating RDoC criteria.

METHODS

Using expert review and consensus, we defined four major depression phenotypes based on specific RDoC constructs. Having matched these constructs to individual items from the Hamilton Depression Rating Scale and Quick Inventory of Depressive Symptomatology, we identified subjects meeting criteria for each of these phenotypes from two large clinical trials of patients treated for major depression. In a post hoc analysis, we evaluated the overall treatment response based on the phenotypes: Core Depression (CD), Anxiety (ANX), and Neurovegetative Symptoms of Melancholia (NVSM) and Atypical Depression (NVSAD).

RESULTS

The phenotypes were prevalent (range 10.5-52.4%, 50% reduction range 51.9-82.9%) and tracked with overall treatment response. Although the CD phenotype was associated with lower rates of remission in both cohorts, this was mainly driven by baseline symptom severity. However, when controlling for baseline severity, patients with the ANX phenotype had a significantly lower rate of remission.

LIMITATIONS

The lack of replication between the studies of the phenotypes' treatment prediction value reflects important variability across studies that may limit generalizability.

CONCLUSION

Further work evaluating biological markers associated with these phenotypes is needed for further RDoC concept development.

The Mechanism of Action of Vagus Nerve Stimulation in Treatment-Resistant Depression: Current Conceptualizations

Stimulation of the left cervical vagus nerve, or vagus nerve stimulation (VNS), brings about an antidepressant response in a subset of treatment-resistant depression (TRD) patients. How this occurs is poorly understood; however, knowledge of the neuroanatomic vagal pathways, in conjunction with functional brain imaging studies, suggests several brain regions associated with mood regulation are critical: brainstem nuclei (locus coeruleus, dorsal raphe, and ventral tegmental area), thalamus, and insular and prefrontal cortex. Furthermore, animal studies suggest that VNS enhances neuroplasticity and changes in neuronal firing patterns. Continued study to better understand the mechanism of action of VNS in TRD is warranted.

Differentially expressed genes related to major depressive disorder and antidepressant response: genome-wide gene expression analysis

Treatment response to antidepressants is limited and varies among patients with major depressive disorder (MDD). To discover genes and mechanisms related to the pathophysiology of MDD and antidepressant treatment response, we performed gene expression analyses using peripheral blood specimens from 38 MDD patients and 14 healthy individuals at baseline and at 6 weeks after the initiation of either selective serotonin reuptake inhibitor (SSRI) or mirtazapine treatment. The results were compared with results from public microarray data. Seven differentially expressed genes (DEGs) between MDD patients and controls were identified in our study and in the public microarray data: CD58, CXCL8, EGF, TARP, TNFSF4, ZNF583, and ZNF587. CXCL8 was among the top 10 downregulated genes in both studies. Eight genes related to SSRI responsiveness, including BTNL8, showed alterations in gene expression in MDD. The expression of the FCRL6 gene differed between SSRI responders and nonresponders and changed after SSRI treatment compared to baseline. In evaluating the response to mirtazapine, 21 DEGs were identified when comparing MDD patients and controls and responders and nonresponders. These findings suggest that the pathophysiology of MDD and treatment response to antidepressants are associated with a number of processes, including DNA damage and apoptosis, that can be induced by immune activation and inflammation.

Differences in the expression of several genes before and after different antidepressant treatments were found in patients with major depressive disorder (MDD), and may help identify patients most likely to benefit from specific drugs. Researchers in South Korea led by Doh Kwan Kim and Soo-Youn Lee at Samsung Medical Center, Seoul, examined gene expression across the 28,869 genes in 38 patients with MDD and 14 healthy individuals. They also validated their findings using existing databases of gene expression in patients with MDD and healthy controls. The research suggests that genes involved in the immune response and inflammation are significantly alternated in MDD and are predictable in which patients respond well to antidepressants. These findings may help develop new approaches to antidepressant therapies, and assist tailoring of treatment to the specific needs of different patients.

PET and Single-Photon Emission Computed Tomography in Brain Concussion

This article offers an overview of the application of PET and single photon emission computed tomography brain imaging to concussion, a type of mild traumatic brain injury and traumatic brain injury, in general. The article reviews the application of these neuronuclear imaging modalities in cross-sectional and longitudinal studies. Additionally, this article frames the current literature with an overview of the basic physics and radiation exposure risks of each modality.

A morphometric signature of depressive symptoms in unmedicated patients with mood disorders

OBJECTIVE

A growing literature indicates that unipolar depression and bipolar depression are associated with alterations in grey matter volume. However, it is unclear to what degree these patterns of morphometric change reflect symptom dimensions. Here, we aimed to predict depressive symptoms and hypomanic symptoms based on patterns of grey matter volume using machine learning.

METHOD

We used machine learning methods combined with voxel-based morphometry to predict depressive and self-reported hypomanic symptoms from grey matter volume in a sample of 47 individuals with unmedicated unipolar and bipolar depression.

RESULTS

We were able to predict depressive severity from grey matter volume in the anteroventral bilateral insula in both unipolar depression and bipolar depression. Self-reported hypomanic symptoms did not predict grey matter loss with a significant degree of accuracy.

DISCUSSION

The results of this study suggest that patterns of grey matter volume alteration in the insula are associated with depressive symptom severity across unipolar and bipolar depression. Studies using other modalities and exploring other brain regions with a larger sample are warranted to identify other systems that may be associated with depressive and hypomanic symptoms across affective disorders.

Personalized (N-of-1) Trials for Depression: A Systematic Review

PURPOSE/BACKGROUND

Personalized (N-of-1) trials are single-patient, crossover-design trials that may be useful for personalizing the selection of depression treatments. We conducted a systematic review of published N-of-1 trials for depression to determine the feasibility and suitability of this methodology for personalizing depression care.

METHODS/PROCEDURES

Electronic databases were searched from database inception through October 2016. Studies were selected if they enrolled depressed patients, included a within-subject crossover design, and systematically assessed depressive symptoms during the N-of-1 trial.

FINDINGS/RESULTS

Five eligible studies reporting on 47 depressed patients (range, 1-18 patients) were identified. Two studies were conducted among adults with treatment-resistant depression, 1 study among depressed inpatients, and 2 studies among patients from special populations (geriatric nursing home, human immunodeficiency virus-associated encephalopathy). All studies evaluated the effects of pharmacologic treatments (methylphenidate, D-amphetamine, ketamine, and sulpiride). Three studies compared an off-label treatment with placebo, 1 study compared 2 off-label treatments, and 1 study compared escalating doses of an off-label treatment with placebo. All 4 studies with more than 1 participant demonstrated heterogeneous treatment effects. All studies produced data that could personalize treatment selection for individual patients. No studies reported on recruitment challenges, compliance with self-tracking, or satisfaction with participation.

IMPLICATIONS/CONCLUSIONS

The feasibility of N-of-1 trials for depression was demonstrated for a limited number of second-line pharmacologic treatments in treatment-resistant patients or in patients with comorbidities that would have excluded them from conventional randomized controlled trials. Additional research is needed to determine whether N-of-1 trials are suitable for improving the selection of depression treatments in clinical practice.

Pretreatment Rostral Anterior Cingulate Cortex Theta Activity in Relation to Symptom Improvement in Depression: A Randomized Clinical Trial

IMPORTANCE

Major depressive disorder (MDD) remains challenging to treat. Although several clinical and demographic variables have been found to predict poor antidepressant response, these markers have not been robustly replicated to warrant implementation in clinical care. Increased pretreatment rostral anterior cingulate cortex (rACC) theta activity has been linked to better antidepressant outcomes. However, no prior study has evaluated whether this marker has incremental predictive validity over clinical and demographic measures.

OBJECTIVE

To determine whether increased pretreatment rACC theta activity would predict symptom improvement regardless of randomization arm.

DESIGN, SETTING, AND PARTICIPANTS

A multicenter randomized clinical trial enrolled outpatients without psychosis and with chronic or recurrent MDD between July 29, 2011, and December 15, 2015 (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care [EMBARC]). Patients were consecutively recruited from 4 university hospitals: 634 patients were screened, 296 were randomized to receive sertraline hydrochloride or placebo, 266 had electroencephalographic (EEG) recordings, and 248 had usable EEG data. Resting EEG data were recorded at baseline and 1 week after trial onset, and rACC theta activity was extracted using source localization. Intent-to-treat analysis was conducted. Data analysis was performed from October 7, 2016, to January 19, 2018.

INTERVENTIONS

An 8-week course of sertraline or placebo.

MAIN OUTCOMES AND MEASURES

The 17-item Hamilton Rating Scale for Depression score (assessed at baseline and weeks 1, 2, 3, 4, 6, and 8).

RESULTS

The 248 participants (160 [64.5%] women, 88 [35.5%] men) with usable EEG data had a mean (SD) age of 36.75 (13.15) years. Higher rACC theta activity at both baseline (b = -1.05; 95% CI, -1.77 to -0.34; P = .004) and week 1 (b = -0.83; 95% CI, -1.60 to -0.06; P < .04) predicted greater depressive symptom improvement, even when controlling for clinical and demographic variables previously linked with treatment outcome. These effects were not moderated by treatment arm. The rACC theta marker, in combination with clinical and demographic variables, accounted for an estimated 39.6% of the variance in symptom change (with 8.5% of the variance uniquely attributable to the rACC theta marker).

CONCLUSIONS AND RELEVANCE

Increased pretreatment rACC theta activity represents a nonspecific prognostic marker of treatment outcome. This is the first study to date to demonstrate that rACC theta activity has incremental predictive validity.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT01407094.

Neuroimaging biomarkers as predictors of treatment outcome in Major Depressive Disorder

BACKGROUND

Current practice for selecting pharmacological and non-pharmacological antidepressant treatments has yielded low response and remission rates in Major Depressive Disorder (MDD). Neuroimaging biomarkers of brain structure and function may be useful in guiding treatment selection by predicting response vs. non-response outcomes.

METHODS

In this review, we summarize data from studies examining predictors of treatment response using structural and functional neuroimaging modalities, as they pertain to pharmacotherapy, psychotherapy, and stimulation treatment strategies. A literature search was conducted in OVID Medline, EMBASE, and PsycINFO databases with coverage from January 1990 to January 2017.

RESULTS

Several imaging biomarkers of therapeutic response in MDD emerged: frontolimbic regions, including the prefrontal cortex, anterior cingulate cortex, hippocampus, amygdala, and insula were regions of interest. Since these sub-regions are implicated in the etiology of MDD, their association with response outcomes may be the result of treatments having a normalizing effect on structural or activation abnormalities.

LIMITATIONS

The direction of findings is inconsistent in studies examining these biomarkers, and variation across 'biotypes' within MDD may account for this. Limitations in sample size and differences in methodology likely also contribute.

CONCLUSIONS

The identification of accurate, reliable neuroimaging biomarkers of treatment response holds promise toward improving treatment outcomes and reducing burden of illness for patients with MDD. However, before these biomarkers can be translated into clinical practice, they will need to be replicated and validated in large, independent samples, and integrated with data from other biological systems.

Indicators to facilitate the early identification of patients with major depressive disorder in need of highly specialized care: A concept mapping study

BACKGROUND

Early identification of the subgroup of patients with major depressive disorder (MDD) in need of highly specialized care could enhance personalized intervention. This, in turn, may reduce the number of treatment steps needed to achieve and sustain an adequate treatment response. The aim of this study was to identify patient-related indicators that could facilitate the early identification of the subgroup of patients with MDD in need of highly specialized care.

METHODS

Initial patient indicators were derived from a systematic review. Subsequently, a structured conceptualization methodology known as concept mapping was employed to complement the initial list of indicators by clinical expertise and develop a consensus-based conceptual framework. Subject-matter experts were invited to participate in the subsequent steps (brainstorming, sorting, and rating) of the concept mapping process. A final concept map solution was generated using nonmetric multidimensional scaling and agglomerative hierarchical cluster analyses.

RESULTS

In total, 67 subject-matter experts participated in the concept mapping process. The final concept map revealed the following 10 major clusters of indicators: 1-depression severity, 2-onset and (treatment) course, 3-comorbid personality disorder, 4-comorbid substance use disorder, 5-other psychiatric comorbidity, 6-somatic comorbidity, 7-maladaptive coping, 8-childhood trauma, 9-social factors, and 10-psychosocial dysfunction.

CONCLUSIONS

The study findings highlight the need for a comprehensive assessment of patient indicators in determining the need for highly specialized care, and suggest that the treatment allocation of patients with MDD to highly specialized mental healthcare settings should be guided by the assessment of clinical and nonclinical patient factors.

Towards characterizing the regional cerebral perfusion in evaluating the severity of major depression disorder with SPECT/CT

BACKGROUND

Major depressive disorder (MDD) is a common mental disorder worldwide, but now there is a lack of clinically effective assessment and management of MDD. In this study, we used technetium-99 m ethylcysteinate dimer ([^99mTc]ECD) SPECT/CT to characterize the regional cerebral blood flow (rCBF) status of MDD patients, and to explore an objective image assessment model of MDD which is non- or minimally-invasive, convenient and accurate in a clinical setting.

METHODS

The severity of MDD was assessed by three trained psychiatrists, based on scores obtained from HAMD and HAMA. [^99mTc]ECD rCBF SPECT/CT was performed in 20 healthy controls and 74 unipolar MDD patients before receiving the treatment. The CT attenuation-corrected SPECT images data were automatically registered, analyzed simultaneously by 3D-SSP and eZIS.

RESULTS

The mean score of HAMD and HAMA in the MDD patients was 25.49 ± 6.00, and 23.12 ± 5.83, respectively. There was a positive correlation between two scores. The MDD women had higher HAMD scores than MDD men. The decreased rCBF of MDD patients in frontal lobes (bilateral B11, B47 and right B4, B6, B10, B46), temporal lobe (right B21, B41, B42) and cingulated cortex (bilateral B24, B33), while their increased rCBF in occipital lobe (bilateral B17, B19 and left B18). Additionally, the depression severity was negatively correlated with decreased rCBF in left ventral anterior cingulate cortex B24, and was positively correlated with decreased rCBF in left inferior prefrontal gyrus B47 and increased rCBF in right associative visual cortex B19. The anxiety severity was negatively correlated with decreased rCBF in left subgenual cortex B25.

CONCLUSIONS

Although the mechanism underlying the correlation is not yet fully understood, our findings indicated that the rCBF SPECT/CT may provide an objective assessment for MDD severity. It might be used monitoring therapeutic efficacy in the management of MDD.

Classification of Depression, Cognitive Disorders, and Co-Morbid Depression and Cognitive Disorders with Perfusion SPECT Neuroimaging

BACKGROUND

Depression and cognitive disorders (CDs) are two common co-morbid afflictions that commonly present with overlapping symptoms.

OBJECTIVE

To evaluate if perfusion neuroimaging with brain SPECT can distinguish persons with depression from those with CDs or both conditions.

METHODS

Inclusion criteria were DSM-IV defined depression or CDs (Alzheimer's disease, vascular dementia, dementia not otherwise specified, and amnestic disorders not otherwise specified) including persons with both (total n = 4,541; 847 CDs, 3,269 depression, 425 with both). Perfusion differences between the groups were calculated using two-sampled t-tests corrected for multiple comparisons. Diagnostic separation was determined with discriminant analysis. Feature selection revealed predictive regions in delineating depression from CDs and comorbid cases.

RESULTS

Persons with CDs had lower cerebral perfusion compared to depression. In co-morbid persons, cerebral hypoperfusion was additive, with regions showing lower regional cerebral blood flow compared to either diagnosis alone. Both baseline and concentration SPECT regions yielded correct classification of 86% and leave one out cross-validation of 83%. AUC analysis for SPECT regions showed 86% accuracy, 80% sensitivity and 75% specificity. Discriminant analysis separated depression and CDs from comorbid cases with correct classification of 90.8% and cross validated accuracy of 88.6%. Area under the curve was 83% with sensitivity of 80% and specificity of 70%. Feature selection identified the most predictive regions in left hippocampus, right insula, cerebellar, and frontal lobe regions.

CONCLUSION

Quantitative perfusion SPECT neuroimaging distinguishes depression from dementia and those with both co-morbidities. Perfusion brain SPECT can be utilized clinically to delineate between these two disorders.

Neural and behavioral correlates of negative self-focused thought associated with depression

A central feature of major depression (MDD) is heightened negative self-focused thought (negative-SFT). Neuroscientific research has identified abnormalities in a network of brain regions in MDD, including brain areas associated with SFT such as medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC). To our knowledge no studies have investigated the behavioral and neural correlates of negative-SFT using a sentence completion task in a sample of individuals with varying depression histories and severities. We test the following hypotheses: (1) negative-SFT will be associated with depression; and (2) depression and negative-SFT will be related to resting-state functional connectivity (rsFC) for brain regions implicated in SFT. Seventy-nine women with varying depression histories and severities completed a sentence completion task and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Standard seed-based voxelwise rsFC was conducted for self-network regions of interest: dorsomedial PFC (dmPFC) and pregenual ACC (pgACC). We performed linear regression analyses to examine the relationships among depression, negative-SFT, and rsFC for the dmPFC and pgACC. Greater negative-SFT was associated with depression history and severity. Greater negative-SFT predicted increased rsFC between dmPFC and pgACC seeds and dorsolateral prefrontal (dlPFC) and parietal regions; depression group was also associated with increased pgACC-dlPFC connectivity. These findings are consistent with previous literature reporting elevated negative-SFT thought in MDD. Our rs-fMRI results provide novel support linking negative-SFT with increased rsFC between self-network and frontoparietal network regions across different levels of depression. Broadly, these findings highlight a dimension of social-affective functioning that may underlie MDD and other psychiatric conditions.

Orbital and Medial Prefrontal Cortex Functional Connectivity of Major Depression Vulnerability and Disease

BACKGROUND

Pathophysiology models of major depression (MD) center on the dysfunction of various cortical areas within the orbital and medial prefrontal cortex. While independent structural and functional abnormalities in these areas are consistent findings in MD, the complex interactions among them and the rest of the cortex remain largely unexplored.

METHODS

We used resting-state functional magnetic resonance imaging connectivity to systematically map alterations in the communication between orbital and medial prefrontal cortex fields and the rest of the brain in MD. Functional connectivity (FC) maps from participants with current MD (n = 35), unaffected first-degree relatives (n = 36), and healthy control subjects (n = 38) were subjected to conjunction analyses to distinguish FC markers of MD vulnerability and FC markers of MD disease.

RESULTS

FC abnormalities in MD vulnerability were found for dorsal medial wall regions and the anterior insula and concerned altered communication of these areas with the inferior parietal cortex and dorsal posterior cingulate, occipital areas and the brainstem. FC aberrations in current MD included the anterior insula, rostral and dorsal anterior cingulate cortex, and lateral orbitofrontal areas and concerned altered communication with the dorsal striatum, the cerebellum, the precuneus, the anterior prefrontal cortex, somatomotor cortex, dorsolateral prefrontal cortex, and visual areas in the occipital and inferior temporal lobes.

CONCLUSIONS

Functionally delineated parcellation maps can be used to identify putative connectivity markers in extended cortical regions such as the orbital and medial prefrontal cortex. The anterior insula and the rostral anterior cingulate cortex play a central role in the pathophysiology of MD, being consistently implicated both in the MD vulnerability and MD disease states.

Neural and neuroendocrine predictors of pharmacological treatment response in adolescents with depression: A preliminary study

OBJECTIVE

Typically, about 30 to 50% of adolescents with depression fail to respond to evidence-based treatments, including antidepressant medications such as selective serotonin reuptake inhibitors (SSRIs). Efforts for identifying predictors and moderators of treatment response are needed to begin to address critical questions relevant to personalized care in adolescent depression. In this pilot study, we aim to identify biological predictors of response to antidepressant treatment.

METHOD

We used a multiple levels of analysis approach to evaluate threat system functioning (fronto-limbic system and the associated hormonal cascade) to determine if key biological indexes at baseline could predict improvement in depressive symptoms after eight weeks of antidepressant treatment in adolescents with depression.

RESULTS

Neural predictors of favorable treatment response included lower amygdala connectivity with left supplementary motor area and with right precentral gyrus, and greater amygdala connectivity with right central opercular cortex and Heschl's gyrus connectivity during rest. During an emotion task, neural predictors of treatment response were greater activation of the bilateral anterior cingulate cortex and left medial frontal gyrus. Additionally, different patterns of salivary cortisol obtained in the context of a modified Trier Social Stress Test were associated with those whose depressive symptoms remitted as compared to those whose symptoms persisted.

CONCLUSIONS

This approach shows significant promise for identifying predictors of treatment response in adolescents with depression. Future work is needed that incorporates sufficiently powered, randomized control trials to provide the basis by which both predictors and moderators of treatment response are identified. The hope is that this work will inform the development of methods that can guide clinician decision-making in assigning beneficial treatments for adolescents who are suffering from depression.

From neuroscience to evidence based psychological treatments - The promise and the challenge, ECNP March 2016, Nice, France

This ECNP meeting was designed to build bridges between different constituencies of mental illness treatment researchers from a range of backgrounds with a specific focus on enhancing the development of novel, evidence based, psychological treatments. In particular we wished to explore the potential for basic neuroscience to support the development of more effective psychological treatments, just as this approach is starting to illuminate the actions of drugs. To fulfil this aim, a selection of clinical psychologists, psychiatrists and neuroscientists were invited to sit at the same table. The starting point of the meeting was the proposition that we know certain psychological treatments work, but we have only an approximate understanding of why they work. The first task in developing a coherent mental health science would therefore be to uncover the mechanisms (at all levels of analysis) of effective psychological treatments. Delineating these mechanisms, a task that will require input from both the clinic and the laboratory, will provide a key foundation for the rational optimisation of psychological treatments. As reviewed in this paper, the speakers at the meeting reviewed recent advances in the understanding of clinical and cognitive psychology, neuroscience, experimental psychopathology, and treatment delivery technology focussed primarily on anxiety disorders and depression. We started by asking three rhetorical questions: What has psychology done for treatment? What has technology done for psychology? What has neuroscience done for psychology? We then addressed how research in five broad research areas could inform the future development of better treatments: Attention, Conditioning, Compulsions and addiction, Emotional Memory, and Reward and emotional bias. Research in all these areas (and more) can be harnessed to neuroscience since psychological therapies are a learning process with a biological basis in the brain. Because current treatment approaches are not fully satisfactory, there is an imperative to understand why not. And when psychological therapies do work we need to understand why this is the case, and how we can improve them. We may be able to improve accessibility to treatment without understanding mechanisms. But for treatment innovation and improvement, mechanistic insights may actually help. Applying neuroscience in this way will become an additional mission for ECNP.

Longitudinal effects of cognitive behavioral therapy for depression on the neural correlates of emotion regulation

Cognitive behavioral therapy (CBT) is effective for a substantial minority of patients suffering from major depressive disorder (MDD), but its mechanism of action at the neural level is not known. As core techniques of CBT seek to enhance emotion regulation, we scanned 31 MDD participants prior to 14 sessions of CBT using functional magnetic resonance imaging (fMRI) and a task in which participants engaged in a voluntary emotion regulation strategy while recalling negative autobiographical memories. Eighteen healthy controls were also scanned. Twenty-three MDD participants completed post-treatment fMRI scanning, and 12 healthy volunteers completed repeat scanning without intervention. Better treatment outcome was associated with longitudinal enhancement of the emotion regulation-dependent BOLD contrast within subgenual anterior cingulate, medial prefrontal cortex, and lingual gyrus. Baseline emotion regulation-dependent BOLD contrast did not predict treatment outcome or differ between MDD and control groups. CBT response may be mediated by enhanced downregulation of neural activity during emotion regulation; brain regions identified overlap with those found using a similar task in a normative sample, and include regions related to self-referential and emotion processing. Future studies should seek to determine specificity of this downregulation to CBT, and evaluate it as a treatment target in MDD.

Predictive Neuroimaging Markers of Psychotherapy Response: A Systematic Review

INTRODUCTION

Predictive neuroimaging markers of treatment response are increasingly sought in order to inform the treatment of major depressive and anxiety disorders. We review the existing literature regarding candidate predictive neuroimaging markers of psychotherapy response and assess their potential clinical utility.

METHODS

We searched Embase, PsycINFO, and PubMed up to October 2014 for studies correlating pretreatment neuroimaging parameters with psychotherapy response in major depressive and anxiety disorders. Our search yielded 40 eligible studies.

RESULTS

The anterior cingulate cortex, amygdala, and anterior insula emerged as potential markers in major depressive disorder and some anxiety disorders. Results across studies displayed a large degree of variability, however, and to date the findings have not been systematically validated in independent clinical cohorts and have not been shown capable of distinguishing between medication and psychotherapy responders. Also limited is the examination of how neuroimaging compares or might add to other prognostic clinical variables.

CONCLUSION

While the extant data suggest avenues of further investigation, we are still far from being able to use these markers clinically. Future studies need to focus on longitudinal testing of potential markers, determining their prescriptive value and examining how they might be integrated with clinical factors.

Activity in the Ventral Medial Prefrontal Cortex Is Necessary for the Therapeutic Effects of Extinction in Rats

Poor response and high relapse rates remain problematic in the treatment of stress-related psychiatric disorders such as depression and post-traumatic stress disorder. Although mechanisms of pharmacotherapies are intensely studied, little is known about mechanisms of behavioral therapy that could inform improved treatments. We have previously demonstrated the therapeutic effects of extinction learning as a behavioral intervention modeling exposure therapy in rats. In the present study, we tested the hypothesis that activity in the ventral medial prefrontal cortex (vmPFC) during extinction is necessary for its therapeutic effects. The inhibitory Gi-coupled designer receptor exclusively activated by designer drug CaMKIIα-hM4Di was expressed in vmPFC before administering chronic unpredictable stress (CUS). vmPFC projection neurons were then inhibited during extinction treatment by administering clozapine-N-oxide. Coping behavior and cognitive flexibility were assessed 24 h later on the shock-probe defensive burying test and attentional set-shifting test, respectively. Replicating previous results, extinction reversed the CUS-induced deficits in coping behavior and cognitive flexibility. Inhibiting vmPFC during extinction blocked these therapeutic effects. Further, increasing vmPFC activity with the excitatory Gq-coupled designer receptor exclusively activated by designer drug hM3Dq 24 h before testing was sufficient to reverse the CUS-induced deficits. CUS reduced mPFC responsivity, assessed by measuring afferent-evoked field potentials in the mPFC, and this reduction was reversed by extinction treatment 24 h before testing. These results demonstrate the necessity of vmPFC activity in the therapeutic effects of extinction as a model of exposure therapy, and suggest that increased vmPFC activity induced by extinction is sufficient to produce lasting plastic changes that underlie its beneficial effects.SIGNIFICANCE STATEMENT Stress-related psychiatric disorders remain poorly treated. Psychotherapies can be effective, but their mechanisms remain unknown, hindering progress toward improved treatment. We used a rat model of behavioral therapy to identify potential targets for enhancing treatment. Fear extinction as a therapeutic behavioral intervention reversed stress-induced cognitive dysfunction and passive coping in rats, modeling components of stress-related psychiatric disease. Extinction also reversed stress-induced attenuation of mPFC responsivity. The therapeutic effects were prevented by blocking activity of glutamatergic neurons in the mPFC during extinction, and were mimicked by inducing activity in lieu of extinction. Thus, activity and plasticity in the mPFC underlie the beneficial effects of extinction on cognitive flexibility and coping behavior compromised by stress, and could be targets to enhance behavioral therapy.

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data

Background: Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge. Methods: Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week post-treatment initiation in 51 patients. Response status at week 12 was established with the Montgomery-Asberg Depression Scale (MADRS), with a ≥50% decrease characterizing responders (N = 27/24 responders/non-responders). We used a machine learning (ML)-approach for predicting response status. We focused on Random Forests, though other ML methods were compared. First, we used a tree-based estimator to select a relatively small number of significant features from: (a) demographic/clinical data (age, sex, individual item/total MADRS scores at baseline, week 1, change scores); (b) scalp-level EEG power; (c) source-localized current density (via exact low-resolution electromagnetic tomography [eLORETA] software). Second, we applied kernel principal component analysis to reduce and map important features. Third, a set of ML models were constructed to classify response outcome based on mapped features. For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features. Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha₁/theta eLORETA features showed the highest predictive value. Eighty-eight scalp EEG features were predictive of response (across bands, both time-points), with theta/alpha₂ being most predictive. Clinical/demographic data consisted of 31 features, with the most important being week 1 "concentration difficulty" scores. When all features were included into one model, its predictive utility was high (88% accuracy). When the most important features were extracted in the final model, 12 predictive features emerged (78% accuracy), including baseline scalp-EEG frontopolar theta, parietal alpha₂ and frontopolar alpha₁. Conclusions: These findings suggest that ML models of pre- and early treatment-emergent EEG profiles and clinical features can serve as tools for predicting antidepressant response. While this must be replicated using large independent samples, it lays the groundwork for research on personalized, "biomarker"-based treatment approaches.

Treatment resistant depression: A multi-scale, systems biology approach

An estimated 50% of depressed patients are inadequately treated by available interventions. Even with an eventual recovery, many patients require a trial and error approach, as there are no reliable guidelines to match patients to optimal treatments and many patients develop treatment resistance over time. This situation derives from the heterogeneity of depression and the lack of biomarkers for stratification by distinct depression subtypes. There is thus a dire need for novel therapies. To address these known challenges, we propose a multi-scale framework for fundamental research on depression, aimed at identifying the brain circuits that are dysfunctional in several animal models of depression as well the changes in gene expression that are associated with these models. When combined with human genetic and imaging studies, our preclinical studies are starting to identify candidate circuits and molecules that are altered both in models of disease and in patient populations. Targeting these circuits and mechanisms can lead to novel generations of antidepressants tailored to specific patient populations with distinctive types of molecular and circuit dysfunction.

Anterior cingulate cortex activity as a candidate biomarker for treatment selection in social anxiety disorder

We aimed to identify biomarkers to guide the decision to add selective serotonin reuptake inhibitors (SSRI) to psychological treatment for social anxiety disorder (SAD). Forty-eight patients with SAD underwent functional magnetic resonance imaging and collection of clinical and demographic variables before treatment with cognitive-behavioural therapy, combined on a double-blind basis with either escitalopram or placebo for 9 weeks. Pre-treatment neural reactivity to aversive faces in the dorsal anterior cingulate cortex (ACC), but not clinical/demographic variables, moderated clinical outcomes. Cross-validated individual-level predictions accurately identified 81% of responders/non-responders. Dorsal ACC reactivity is thus a potential biomarker for SAD treatment selection.

DECLARATION OF INTEREST

None.

The potential of predictive analytics to provide clinical decision support in depression treatment planning

PURPOSE OF REVIEW

To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD).

RECENT FINDINGS

Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem.

SUMMARY

Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

Neurophysiologic effects of transcutaneous auricular vagus nerve stimulation (taVNS) via electrical stimulation of the tragus: A concurrent taVNS/fMRI study and review

BACKGROUND

Electrical stimulation of the auricular branch of the vagus nerve (ABVN) via transcutaneous auricular vagus nerve stimulation (taVNS) may influence afferent vagal networks. There have been 5 prior taVNS/fMRI studies, with inconsistent findings due to variability in stimulation targets and parameters.

OBJECTIVE

We developed a taVNS/fMRI system to enable concurrent electrical stimulation and fMRI acquisition to compare the effects of taVNS in relation to control stimulation.

METHODS

We enrolled 17 healthy adults in this single-blind, crossover taVNS/fMRI trial. Based on parameters shown to affect heart rate in healthy volunteers, participants received either left tragus (active) or earlobe (control) stimulation at 500 μs 25 HZ for 60 s (repeated 3 times over 6 min). Whole brain fMRI analysis was performed exploring the effect of: active stimulation, control stimulation, and the comparison. Region of interest analysis of the midbrain and brainstem was also conducted.

RESULTS

Active stimulation produced significant increased BOLD signal in the contralateral postcentral gyrus, bilateral insula, frontal cortex, right operculum, and left cerebellum. Control stimulation produced BOLD signal activation in the contralateral postcentral gyrus. In the active vs. control contrast, tragus stimulation produced significantly greater BOLD increases in the right caudate, bilateral anterior cingulate, cerebellum, left prefrontal cortex, and mid-cingulate.

CONCLUSION

Stimulation of the tragus activates the cerebral afferents of the vagal pathway and combined with our review of the literature suggest that taVNS is a promising form of VNS. Future taVNS/fMRI studies should systematically explore various parameters and alternative stimulation targets aimed to optimize this novel form of neuromodulation.

Common genes associated with antidepressant response in mouse and man identify key role of glucocorticoid receptor sensitivity

Response to antidepressant treatment in major depressive disorder (MDD) cannot be predicted currently, leading to uncertainty in medication selection, increasing costs, and prolonged suffering for many patients. Despite tremendous efforts in identifying response-associated genes in large genome-wide association studies, the results have been fairly modest, underlining the need to establish conceptually novel strategies. For the identification of transcriptome signatures that can distinguish between treatment responders and nonresponders, we herein submit a novel animal experimental approach focusing on extreme phenotypes. We utilized the large variance in response to antidepressant treatment occurring in DBA/2J mice, enabling sample stratification into subpopulations of good and poor treatment responders to delineate response-associated signature transcript profiles in peripheral blood samples. As a proof of concept, we translated our murine data to the transcriptome data of a clinically relevant human cohort. A cluster of 259 differentially regulated genes was identified when peripheral transcriptome profiles of good and poor treatment responders were compared in the murine model. Differences in expression profiles from baseline to week 12 of the human orthologues selected on the basis of the murine transcript signature allowed prediction of response status with an accuracy of 76% in the patient population. Finally, we show that glucocorticoid receptor (GR)-regulated genes are significantly enriched in this cluster of antidepressant-response genes. Our findings point to the involvement of GR sensitivity as a potential key mechanism shaping response to antidepressant treatment and support the hypothesis that antidepressants could stimulate resilience-promoting molecular mechanisms. Our data highlight the suitability of an appropriate animal experimental approach for the discovery of treatment response-associated pathways across species.

Major depression is the second leading cause of disability worldwide. However, only one-third of patients with depression benefit from the first antidepressant compound they are prescribed. It is a fundamental problem that the outcomes of individual antidepressant treatments are still highly unpredictable. In clinical studies, discovery of biomarkers for antidepressant response is hampered by confounding factors such as the heterogeneity of the disease phenotype and additional environmental factors, e.g., previous life events and different schedules of psychopharmacological treatment, which reduce the power to detect true response biomarkers. To overcome some of these limitations, we have established a conceptually novel approach that allows the selection of extreme phenotypes in an antidepressant-responsive mouse strain. In the first step, we identify signatures in the transcriptome of peripheral blood associated with responses following stratification into good and poor treatment responders. As proof of concept, we translate the murine data to a population of depressed patients. We show that differences in expression profiles from baseline to week 12 of the human orthologues predict response status in patients. We finally provide evidence that sensitivity of the glucocorticoid receptor could be a potential key mechanism shaping response to antidepressant treatment.

Hyper-responsivity to losses in the anterior insula during economic choice scales with depression severity

BACKGROUND

Commonly observed distortions in decision-making among patients with major depressive disorder (MDD) may emerge from impaired reward processing and cognitive biases toward negative events. There is substantial theoretical support for the hypothesis that MDD patients overweight potential losses compared with gains, though the neurobiological underpinnings of this bias are uncertain.

METHODS

Twenty-one unmedicated patients with MDD were compared with 25 healthy controls (HC) using functional magnetic resonance imaging (fMRI) together with an economic decision-making task over mixed lotteries involving probabilistic gains and losses. Region-of-interest analyses evaluated neural signatures of gain and loss coding within a core network of brain areas known to be involved in valuation (anterior insula, caudate nucleus, ventromedial prefrontal cortex).

RESULTS

Usable fMRI data were available for 19 MDD and 23 HC subjects. Anterior insula signal showed negative coding of losses (gain > loss) in HC subjects consistent with previous findings, whereas MDD subjects demonstrated significant reversals in these associations (loss > gain). Moreover, depression severity further enhanced the positive coding of losses in anterior insula, ventromedial prefrontal cortex, and caudate nucleus. The hyper-responsivity to losses displayed by the anterior insula of MDD patients was paralleled by a reduced influence of gain, but not loss, stake size on choice latencies.

CONCLUSIONS

Patients with MDD demonstrate a significant shift from negative to positive coding of losses in the anterior insula, revealing the importance of this structure in value-based decision-making in the context of emotional disturbances.

Neuroimaging Advances for Depression

Depression is one of the world's most prevalent mental health problems, with as many as 350 million sufferers worldwide and close to 20 million sufferers in the US. While neuroimaging applications for identifying various types of depression have made enormous strides in recent years, no findings have been sufficiently replicated or considered significant enough to warrant application in clinical settings. Our authors are well equipped to tell us what the future may bring.

Basolateral amygdala-ventromedial prefrontal cortex connectivity predicts cognitive behavioural therapy outcome in adults with obsessive-compulsive disorder

BACKGROUND

Cognitive behavioural therapy (CBT), including exposure and ritual prevention, is a first-line treatment for obsessive-compulsive disorder (OCD), but few reliable predictors of CBT outcome have been identified. Based on research in animal models, we hypothesized that individual differences in basolateral amygdala-ventromedial prefrontal cortex (BLA-vmPFC) communication would predict CBT outcome in patients with OCD.

METHODS

We investigated whether BLA-vmPFC resting-state functional connectivity (rs-fc) predicts CBT outcome in patients with OCD. We assessed BLA-vmPFC rs-fc in patients with OCD on a stable dose of a selective serotonin reuptake inhibitor who then received CBT and in healthy control participants.

RESULTS

We included 73 patients with OCD and 84 healthy controls in our study. Decreased BLA-vmPFC rs-fc predicted a better CBT outcome in patients with OCD and was also detected in those with OCD compared with healthy participants. Additional analyses revealed that decreased BLA-vmPFC rs-fc uniquely characterized the patients with OCD who responded to CBT.

LIMITATIONS

We used a sample of convenience, and all patients were receiving pharmacological treatment for OCD.

CONCLUSION

In this large sample of patients with OCD, BLA-vmPFC functional connectivity predicted CBT outcome. These results suggest that future research should investigate the potential of BLA-vmPFC pathways to inform treatment selection for CBT across patients with OCD and anxiety disorders.

Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis

Finding robust brain substrates of mood disorders is an important target for research. The degree to which major depression (MDD) and bipolar disorder (BD) are associated with common and/or distinct patterns of volumetric changes is nevertheless unclear. Furthermore, the extant literature is heterogeneous with respect to the nature of these changes. We report a meta-analysis of voxel-based morphometry (VBM) studies in MDD and BD. We identified studies published up to January 2015 that compared grey matter in MDD (50 data sets including 4101 individuals) and BD (36 data sets including 2407 individuals) using whole-brain VBM. We used statistical maps from the studies included where available and reported peak coordinates otherwise. Group comparisons and conjunction analyses identified regions in which the disorders showed common and distinct patterns of volumetric alteration. Both disorders were associated with lower grey-matter volume relative to healthy individuals in a number of areas. Conjunction analysis showed smaller volumes in both disorders in clusters in the dorsomedial and ventromedial prefrontal cortex, including the anterior cingulate cortex and bilateral insula. Group comparisons indicated that findings of smaller grey-matter volumes relative to controls in the right dorsolateral prefrontal cortex and left hippocampus, along with cerebellar, temporal and parietal regions were more substantial in major depression. These results suggest that MDD and BD are characterised by both common and distinct patterns of grey-matter volume changes. This combination of differences and similarities has the potential to inform the development of diagnostic biomarkers for these conditions.

Gamma-Hydroxybutyrate Increases Resting-State Limbic Perfusion and Body and Emotion Awareness in Humans

Gamma-hydroxybutyrate (GHB) is a GHB-/GABA-B receptor agonist inducing a broad spectrum of subjective effects including euphoria, disinhibition, and enhanced vitality. It is used as treatment for neuropsychiatric disorders including narcolepsy and alcohol withdrawal, but is also a drug of abuse. Non-medical users report enhancement of body and emotion awareness during intoxication. However, the neuronal underpinnings of such awareness alterations under GHB are unknown so far. The assessment of regional cerebral blood flow (rCBF) by pharmacological magnetic resonance imaging (phMRI) enables the elucidation of drug-induced functional brain alterations. Thus, we assessed the effects of GHB (35 mg/kg p.o.) in 17 healthy males on rCBF and subjective drug effects, using a placebo-controlled, double-blind, randomized, cross-over design employing arterial spin labeling phMRI. Compared to placebo, GHB increased subjective ratings for body and emotion awareness, and for dizziness (p<0.01-0.001, Bonferroni-corrected). A whole-brain analysis showed increased rCBF in the bilateral anterior cingulate cortex (ACC) and the right anterior insula under GHB (p<0.05, cluster-corrected). ACC and insula rCBF are correlated with relaxation, and body and emotion awareness (p<0.05-0.001, uncorrected). Interaction analyses revealed that GHB-induced increase of body awareness was accompanied by increased rCBF in ACC, whereas relaxation under GHB was accompanied by elevated rCBF in right anterior insula (p<0.05, uncorrected). In conclusion, enhancement of emotion and body awareness, and increased perfusion of insula and ACC bears implications both for the properties of GHB as a drug of abuse as well as for its putative personalized potential for specific therapeutic indications in affective disorders.

Brain-Based Biomarkers for the Treatment of Depression: Evolution of an Idea

An ambition of depression biomarker research is to augment psychometric and cognitive assessment of clinically relevant phenomena with neural measures. Although such applications have been slow to arrive, we observe a steady evolution of the idea and anticipate emerging technologies with some optimism. To highlight critical themes and innovations in depression biomarker research, we take as our point of reference a specific research narrative. We begin with an early model of frontal-limbic dysfunction, which represents a conceptual shift from localized pathology to understanding symptoms as an emergent property of distributed networks. Over the decades, this model accommodates perspectives from neurology, psychiatry, clinical, and cognitive neuroscience, and preserves past insight as more complex methods become available. We also track the expanding mission of brain biomarker research: from the development of diagnostic tools to treatment selection algorithms, measures of neurocognitive functioning and novel targets for neuromodulation. To conclude, we draw from this particular research narrative future directions for biomarker research. We emphasize integration of measurement modalities to describe dynamic change in domain-general networks, and we speculate that a brain-based framework for psychiatric problems may dissolve classical diagnostic and disciplinary boundaries. (JINS, 2017, 23, 870-880).

Altered right anterior insular connectivity and loss of associated functions in adolescent chronic fatigue syndrome

Impairments in cognition, pain intolerance, and physical inactivity characterize adolescent chronic fatigue syndrome (CFS), yet little is known about its neurobiology. The right dorsal anterior insular (dAI) connectivity of the salience network provides a motivational context to stimuli. In this study, we examined regional functional connectivity (FC) patterns of the right dAI in adolescent CFS patients and healthy participants. Eighteen adolescent patients with CFS and 18 aged-matched healthy adolescent control participants underwent resting-state functional magnetic resonance imaging. The right dAI region of interest was examined in a seed-to-voxel resting-state FC analysis using SPM and CONN toolbox. Relative to healthy adolescents, CFS patients demonstrated reduced FC of the right dAI to the right posterior parietal cortex (PPC) node of the central executive network. The decreased FC of the right dAI–PPC might indicate impaired cognitive control development in adolescent CFS. Immature FC of the right dAI–PPC in patients also lacked associations with three known functional domains: cognition, pain and physical activity, which were observed in the healthy group. These results suggest a distinct biological signature of adolescent CFS and might represent a fundamental role of the dAI in motivated behavior.

Abnormal functional connectivity within resting-state networks is related to rTMS-based therapy effects of treatment resistant depression: A pilot study

BACKGROUND

Treatment resistant depression (TRD) remains a clinical challenge, and finding biomarkers that predict treatment response are a long sought goal to precisely indicate treatments. This pilot study aims to characterize brain dysfunction in TRD patients who underwent rTMS to define neuroimaging biomarkers that discriminate non-responders (NR) from responders (R).

METHODS

20 TRD patients who underwent a course of rTMS to the left DLPFC were categorized into R and NR groups based on a >50% reduction in HRSD scores. Utilizing resting-state fMRI and ICA techniques, this study compared baseline RSNs of R vs. NR as well as TRD vs. healthy volunteer group. Regression analysis was conducted to link regions with clinical improvements. ROC analysis was further conducted to confirm the utility of the identified regions in classifying the patients.

RESULTS

Prior to treatment, non-responders displayed hyper-connectivity in ACC/VMPFC, PCC/pC, dACC and insula within RSNs that have been associated with MDD pathology. Regression results showed that regions associated with clinical improvements overlapped largely with regions that showed aberrant connectivity. ACC/VMPFC, dACC and left insula, which are hub regions of DMN and SN, exhibited excellent performance (highest sensitivity=100% and highest specificity=82%) in discriminating the response status of the patients.

LIMITATIONS

Relatively small sample size.

CONCLUSIONS

Our findings provide insight into fMRI predictive measures of treatment response to rTMS treatment, and demonstrate the potential of RSNs-based biomarkers in predicting response to rTMS treatment. Future studies are needed to validate the application of these measures to inform individual treatment indications.

Fractional amplitude of low-frequency fluctuations (fALFF) in post-stroke depression

Depression is a common outcome following stroke, associated with reduced quality of life and poorer recovery. Despite attempts to associate depression symptoms with specific lesion sites, the neural basis of post-stroke depression remains poorly understood. Resting state fMRI has provided new insights into the neural underpinnings of post-stroke depression, but has been limited to connectivity analyses exploring interregional correlations in the time-course of activity. Other aspects of resting state BOLD signal remain unexamined. Measuring the amplitude of low frequency fluctuations allows the detection of spontaneous neural activity across the whole brain. It provides complementary information about frequency-specific local neural activity. We calculated the fractional amplitude of low frequency fluctuations (fALFF) in a group of 64 participants scanned 3 months post-stroke. Twenty showed depression symptoms when assessed with the Patient Health Questionnaire (PHQ-9). We performed analyses in both the typical 0.01-0.08 Hz range, as well as separately in the slow-5 (0.01-0.027 Hz) and slow-4 (0.027-0.073 Hz) ranges. We found significantly higher fALFF in the depressed compared to non-depressed participants in the left dorsolateral prefrontal cortex (DLPFC) and the right precentral gyrus, and a significant association between higher depression scores and higher fALFF in the left insula. The group differences were detected in the slow-5 fluctuations, while the association with depression severity was observed in the slow-4 range. We conclude that post-stroke depression can be characterised by aberrant spontaneous local neural activity, which in small samples could be a more sensitive measure than lesion volume and location.

Insula and amygdala resting-state functional connectivity differentiate bipolar from unipolar depression

OBJECTIVE

Distinguishing depressive episodes due to bipolar disorder (BD) or major depressive disorder (MDD) solely on clinical grounds is challenging. We aimed at comparing resting-state functional connectivity (rsFC) of regions subserving emotional regulation in similarly depressed BD and MDD.

METHOD

We enrolled 76 in-patients (BD, n = 36; MDD, n = 40) and 40 healthy controls (HC). A seed-based approach was used to identify regions showing different rsFC with the insula and the amygdala. Insular and amygdalar parcellations were then performed along with diagnostic accuracy of the main findings.

RESULTS

Lower rsFC between the left insula and the left mid-dorsolateral prefrontal cortex and between bilateral insula and right frontopolar prefrontal cortex (FPPFC) was observed in BD compared to MDD and HC. These results were driven by the dorsal anterior and posterior insula (PI). Lower rsFC between the right amygdala and the left anterior hippocampus was observed in MDD compared to BD and HC. These results were driven by the centromedial and laterobasal amygdala. Left PI/right FPPC rsFC showed 78% accuracy differentiating BD and MDD.

CONCLUSION

rsFC of amygdala and insula distinguished between depressed BD and MDD. The observed differences suggest the possibility of differential pathophysiological mechanisms of emotional dysfunction in bipolar and unipolar depression.

Quantitative positron emission tomography in brain research

The application of positron emission tomography (PET) in brain research has increased substantially during the past 20years, and is still growing. PET provides a unique insight into physiological and pathological processes in vivo. In this article we introduce the fundamentals of PET, and the methods available for acquiring quantitative estimates of the parameters of interest. A short introduction to different areas of application is also given, including basic research of brain function and in neurology, psychiatry, drug receptor occupancy studies, and its application in diagnostics of neurodegenerative disorders such as Alzheimer's disease and Parkinson's disease. Our aim is to inform the unfamiliar reader of the underlying basics and potential applications of PET, hoping to inspire the reader into considering how the technique could be of benefit for his or her own research.

Effects of Patient Preferences on Outcomes in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) Study

OBJECTIVE

The Predictors of Remission in Depression to Individual and Combined Treatments [PReDICT] study aimed to identify clinical and biological factors predictive of treatment outcomes in major depressive disorder among treatment-naive adults. The authors evaluated the efficacy of cognitive-behavioral therapy (CBT) and two antidepressant medications (escitalopram and duloxetine) in patients with major depression and examined the moderating effect of patients' treatment preferences on outcomes.

METHOD

Adults aged 18-65 with treatment-naive major depression were randomly assigned with equal likelihood to 12 weeks of treatment with escitalopram (10-20 mg/day), duloxetine (30-60 mg/day), or CBT (16 50-minute sessions). Prior to randomization, patients indicated whether they preferred medication or CBT or had no preference. The primary outcome was change in the 17-item Hamilton Depression Rating Scale (HAM-D), administered by raters blinded to treatment.

RESULTS

A total of 344 patients were randomly assigned, with a mean baseline HAM-D score of 19.8 (SD=3.8). The mean estimated overall decreases in HAM-D score did not significantly differ between treatments (CBT: 10.2, escitalopram: 11.1, duloxetine: 11.2). Last observation carried forward remission rates did not significantly differ between treatments (CBT: 41.9%, escitalopram: 46.7%, duloxetine: 54.7%). Patients matched to their preferred treatment were more likely to complete the trial but not more likely to achieve remission.

CONCLUSIONS

Treatment guidelines that recommend either an evidence-based psychotherapy or antidepressant medication for nonpsychotic major depression can be extended to treatment-naive patients. Treatment preferences among patients without prior treatment exposure do not significantly moderate symptomatic outcomes.

Functional Connectivity of the Subcallosal Cingulate Cortex And Differential Outcomes to Treatment With Cognitive-Behavioral Therapy or Antidepressant Medication for Major Depressive Disorder

OBJECTIVE

The purpose of this article was to inform the first-line treatment choice between cognitive-behavioral therapy (CBT) or an antidepressant medication for treatment-naive adults with major depressive disorder by defining a neuroimaging biomarker that differentially identifies the outcomes of remission and treatment failure to these interventions.

METHOD

Functional MRI resting-state functional connectivity analyses using a bilateral subcallosal cingulate cortex (SCC) seed was applied to 122 patients from the Prediction of Remission to Individual and Combined Treatments (PReDICT) study who completed 12 weeks of randomized treatment with CBT or antidepressant medication. Of the 122 participants, 58 achieved remission (Hamilton Depression Rating Scale [HAM-D] score ≤7 at weeks 10 and 12), and 24 had treatment failure (<30% decrease from baseline in HAM-D score). A 2×2 analysis of variance using voxel-wise subsampling permutation tests compared the interaction of treatment and outcome. Receiver operating characteristic curves constructed using brain connectivity measures were used to determine possible classification rates for differential treatment outcomes.

RESULTS

The resting-state functional connectivity of the following three regions with the SCC was differentially associated with outcomes of remission and treatment failure to CBT and antidepressant medication and survived application of the subsample permutation tests: the left anterior ventrolateral prefrontal cortex/insula, the dorsal midbrain, and the left ventromedial prefrontal cortex. Using the summed SCC functional connectivity scores for these three regions, overall classification rates of 72%-78% for remission and 75%-89% for treatment failure was demonstrated. Positive summed functional connectivity was associated with remission with CBT and treatment failure with medication, whereas negative summed functional connectivity scores were associated with remission to medication and treatment failure with CBT.

CONCLUSIONS

Imaging-based depression subtypes defined using resting-state functional connectivity differentially identified an individual's probability of remission or treatment failure with first-line treatment options for major depression. This biomarker should be explored in future research through prospective testing and as a component of multivariate treatment prediction models.

How to Perform and Interpret Functional Magnetic Resonance Imaging Studies in Functional Gastrointestinal Disorders

Functional neuroimaging studies have revealed the importance of the role of cognitive and psychological factors and the dysregulation of the brain-gut axis in functional gastrointestinal disorder patients. Although only a small number of neuroimaging studies have been conducted in functional gastrointestinal disorder patients, and despite the fact that the neuroimaging technique requires a high level of knowledge, the technique still has a great deal of potential. The application of functional magnetic resonance imaging (fMRI) technique in functional gastrointestinal disorders should provide novel methods of diagnosing and treating patients. In this review, basic knowledge and technical/practical issues of fMRI will be introduced to clinicians.

Right brain, left brain in depressive disorders: Clinical and theoretical implications of behavioral, electrophysiological and neuroimaging findings

The right and left side of the brain are asymmetric in anatomy and function. We review electrophysiological (EEG and event-related potential), behavioral (dichotic and visual perceptual asymmetry), and neuroimaging (PET, MRI, NIRS) evidence of right-left asymmetry in depressive disorders. Recent electrophysiological and fMRI studies of emotional processing have provided new evidence of altered laterality in depressive disorders. EEG alpha asymmetry and neuroimaging findings at rest and during cognitive or emotional tasks are consistent with reduced left prefrontal activity in depressed patients, which may impair downregulation of amygdala response to negative emotional information. Dichotic listening and visual hemifield findings for non-verbal or emotional processing have revealed abnormal perceptual asymmetry in depressive disorders, and electrophysiological findings have shown reduced right-lateralized responsivity to emotional stimuli in occipitotemporal or parietotemporal cortex. We discuss models of neural networks underlying these alterations. Of clinical relevance, individual differences among depressed patients on measures of right-left brain function are related to diagnostic subtype of depression, comorbidity with anxiety disorders, and clinical response to antidepressants or cognitive behavioral therapy.

Treating major depression with yoga: A prospective, randomized, controlled pilot trial

Background

Conventional pharmacotherapies and psychotherapies for major depression are associated with limited adherence to care and relatively low remission rates. Yoga may offer an alternative treatment option, but rigorous studies are few. This randomized controlled trial with blinded outcome assessors examined an 8-week hatha yoga intervention as mono-therapy for mild-to-moderate major depression.

Methods

Investigators recruited 38 adults in San Francisco meeting criteria for major depression of mild-to-moderate severity, per structured psychiatric interview and scores of 14–28 on Beck Depression Inventory-II (BDI). At screening, individuals engaged in psychotherapy, antidepressant pharmacotherapy, herbal or nutraceutical mood therapies, or mind-body practices were excluded. Participants were 68% female, with mean age 43.4 years (SD = 14.8, range = 22–72), and mean BDI score 22.4 (SD = 4.5). Twenty participants were randomized to 90-minute hatha yoga practice groups twice weekly for 8 weeks. Eighteen participants were randomized to 90-minute attention control education groups twice weekly for 8 weeks. Certified yoga instructors delivered both interventions at a university clinic. Primary outcome was depression severity, measured by BDI scores every 2 weeks from baseline to 8 weeks. Secondary outcomes were self-efficacy and self-esteem, measured by scores on the General Self-Efficacy Scale (GSES) and Rosenberg Self-Esteem Scale (RSES) at baseline and at 8 weeks.

Results

In intent-to-treat analysis, yoga participants exhibited significantly greater 8-week decline in BDI scores than controls (p-value = 0.034). In sub-analyses of participants completing final 8-week measures, yoga participants were more likely to achieve remission, defined per final BDI score ≤ 9 (p-value = 0.018). Effect size of yoga in reducing BDI scores was large, per Cohen’s d = -0.96 [95%CI, -1.81 to -0.12]. Intervention groups did not differ significantly in 8-week change scores for either the GSES or RSES.

Conclusion

In adults with mild-to-moderate major depression, an 8-week hatha yoga intervention resulted in statistically and clinically significant reductions in depression severity.

Trial registration

ClinicalTrials.gov NCT01210651

A spectroscopic approach toward depression diagnosis: local metabolism meets functional connectivity

Abnormal anterior insula (AI) response and functional connectivity (FC) is associated with depression. In addition to clinical features, such as severity, AI FC and its metabolism further predicted therapeutic response. Abnormal FC between anterior cingulate and AI covaried with reduced glutamate level within cingulate cortex. Recently, deficient glial glutamate conversion was found in AI in major depression disorder (MDD). We therefore postulate a local glutamatergic mechanism in insula cortex of depressive patients, which is correlated with symptoms severity and itself influences AI's network connectivity in MDD. Twenty-five MDD patients and 25 healthy controls (HC) matched on age and sex underwent resting state functional magnetic resonance imaging and magnetic resonance spectroscopy scans. To determine the role of local glutamate-glutamine complex (Glx) ratio on whole brain AI FC, we conducted regression analysis with Glx relative to creatine (Cr) ratio as factor of interest and age, sex, and voxel tissue composition as nuisance factors. We found that in MDD, but not in HC, AI Glx/Cr ratio correlated positively with AI FC to right supramarginal gyrus and negatively with AI FC toward left occipital cortex (p < 0.05 family wise error). AI Glx/Cr level was negatively correlated with HAMD score (p < 0.05) in MDD patients. We showed that the local AI ratio of glutamatergic-creatine metabolism is an underlying candidate subserving functional network disintegration of insula toward low level and supramodal integration areas, in MDD. While causality cannot directly be inferred from such correlation, our finding helps to define a multilevel network of response-predicting regions based on local metabolism and connectivity strength.

The Structure of Personality Disorders within a Depressed Sample: Implications for Personalizing Treatment

BACKGROUND

Personality disorders (PDs) and major depressive disorder (MDD) are both significant public health burdens. They are frequently comorbid, and this comorbidity predicts poorer treatment outcomes and lower maintenance of treatment effects. Although there is growing consensus on the structure of personality pathology in non-depressed individuals, there is limited research on the structure of personality pathology in individuals experiencing MDD.

METHOD

As part of the Predictors of Remission in Depression to Individual and Combined Treatment (PReDICT) randomized controlled trial, 192 treatment-naïve subjects meeting DSM-IV-TR criteria for MDD completed the International Personality Disorder Examination (IPDE). Using this sample, a principal components analysis explored the factor structure of the IPDE.

RESULTS

A three-factor model comprised three factors labeled "NADA" (Negative Affectivity, Disinhibition, and Antagnoism)," "Social Anxiety," and "Antisociality." Factor intercorrelations were small-to-moderate, and the sum score of the three factors was highly correlated (r = .94) with the total IPDE score.

LIMITATIONS

Personality pathology was assessed with one instrument, and sample size was smaller than ideal for factor analytic research. \.

CONCLUSIONS

Consistent with prior factor-analytic findings, a three-factor solution provided the most clinically and theoretically useful model. This finding lends support for the personality disorders retained in DSM-5 and some support for a model of personality pathology aligned with the personality traits found in the leading nonclinical models of personality. The obtained factors are potential moderators of clinical interventions and may serve as an avenue to personalizing treatments.